{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellargraph example: Graph Attention Network (GAT) on the CORA citation dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import NetworkX and stellar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GAT\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the features and subject for the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=140, test_size=None, stratify=node_data[\"subject\"]\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data, train_size=500, test_size=None, stratify=test_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Neural_Networks': 42,\n",
       "         'Genetic_Algorithms': 22,\n",
       "         'Rule_Learning': 9,\n",
       "         'Case_Based': 16,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Theory': 18,\n",
       "         'Reinforcement_Learning': 11})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GAT model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5278\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5278]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since GAT is a full-batch model, we use the `FullBatchNodeGenerator` class to feed node features and graph adjacency matrix to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = FullBatchNodeGenerator(G, method=\"gat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use two GAT layers with 8-dimensional hidden node features for the first layer and the 7 class classification output for the second layer.\n",
    " * `attn_heads` is the number of attention heads in all but the last GAT layer in the model\n",
    " * `activations` is a list of activations applied to each layer's output\n",
    " * Arguments such as `bias`, `in_dropout`, `attn_dropout` are internal parameters of the model, execute `?GAT` for details. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To follow the GAT model architecture used for Cora dataset in the original paper [Graph Attention Networks. P. Velickovic et al. ICLR 2018 https://arxiv.org/abs/1710.10903], let's build a 2-layer GAT model, with the 2nd layer being the classifier that predicts paper subject: it thus should have the output size of `train_targets.shape[1]` (7 subjects) and a softmax activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "gat = GAT(\n",
    "    layer_sizes=[8, train_targets.shape[1]],\n",
    "    activations=[\"elu\", \"softmax\"],\n",
    "    attn_heads=8,\n",
    "    generator=generator,\n",
    "    in_dropout=0.5,\n",
    "    attn_dropout=0.5,\n",
    "    normalize=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expose the input and output tensors of the GAT model for node prediction, via GAT.build() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, predictions = gat.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `predictions` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create callbacks for early stopping (if validation accuracy stops improving) and best model checkpoint saving:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "if not os.path.isdir(\"logs\"):\n",
    "    os.makedirs(\"logs\")\n",
    "es_callback = EarlyStopping(\n",
    "    monitor=\"val_acc\", patience=20\n",
    ")  # patience is the number of epochs to wait before early stopping in case of no further improvement\n",
    "mc_callback = ModelCheckpoint(\n",
    "    \"logs/best_model.h5\", monitor=\"val_acc\", save_best_only=True, save_weights_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1/1 - 0s - loss: 1.9069 - acc: 0.2000 - val_loss: 1.8187 - val_acc: 0.3540\n",
      "Epoch 2/50\n",
      "1/1 - 0s - loss: 1.7973 - acc: 0.3214 - val_loss: 1.7286 - val_acc: 0.3640\n",
      "Epoch 3/50\n",
      "1/1 - 0s - loss: 1.7595 - acc: 0.3143 - val_loss: 1.6500 - val_acc: 0.3860\n",
      "Epoch 4/50\n",
      "1/1 - 0s - loss: 1.6281 - acc: 0.4286 - val_loss: 1.5803 - val_acc: 0.4080\n",
      "Epoch 5/50\n",
      "1/1 - 0s - loss: 1.4758 - acc: 0.5000 - val_loss: 1.5141 - val_acc: 0.4520\n",
      "Epoch 6/50\n",
      "1/1 - 0s - loss: 1.4778 - acc: 0.4429 - val_loss: 1.4507 - val_acc: 0.5040\n",
      "Epoch 7/50\n",
      "1/1 - 0s - loss: 1.3761 - acc: 0.4929 - val_loss: 1.3885 - val_acc: 0.5540\n",
      "Epoch 8/50\n",
      "1/1 - 0s - loss: 1.3111 - acc: 0.5500 - val_loss: 1.3284 - val_acc: 0.6200\n",
      "Epoch 9/50\n",
      "1/1 - 0s - loss: 1.2383 - acc: 0.5929 - val_loss: 1.2704 - val_acc: 0.6760\n",
      "Epoch 10/50\n",
      "1/1 - 0s - loss: 1.1667 - acc: 0.6714 - val_loss: 1.2157 - val_acc: 0.7220\n",
      "Epoch 11/50\n",
      "1/1 - 0s - loss: 1.0948 - acc: 0.6929 - val_loss: 1.1655 - val_acc: 0.7580\n",
      "Epoch 12/50\n",
      "1/1 - 0s - loss: 1.0069 - acc: 0.7857 - val_loss: 1.1177 - val_acc: 0.7920\n",
      "Epoch 13/50\n",
      "1/1 - 0s - loss: 1.0820 - acc: 0.7286 - val_loss: 1.0733 - val_acc: 0.8040\n",
      "Epoch 14/50\n",
      "1/1 - 0s - loss: 1.0289 - acc: 0.7000 - val_loss: 1.0323 - val_acc: 0.8260\n",
      "Epoch 15/50\n",
      "1/1 - 0s - loss: 0.8989 - acc: 0.7500 - val_loss: 0.9941 - val_acc: 0.8340\n",
      "Epoch 16/50\n",
      "1/1 - 0s - loss: 0.9235 - acc: 0.7786 - val_loss: 0.9605 - val_acc: 0.8300\n",
      "Epoch 17/50\n",
      "1/1 - 0s - loss: 0.8083 - acc: 0.8571 - val_loss: 0.9292 - val_acc: 0.8280\n",
      "Epoch 18/50\n",
      "1/1 - 0s - loss: 0.9297 - acc: 0.7429 - val_loss: 0.8996 - val_acc: 0.8340\n",
      "Epoch 19/50\n",
      "1/1 - 0s - loss: 0.7311 - acc: 0.8643 - val_loss: 0.8709 - val_acc: 0.8400\n",
      "Epoch 20/50\n",
      "1/1 - 0s - loss: 0.7722 - acc: 0.8143 - val_loss: 0.8445 - val_acc: 0.8400\n",
      "Epoch 21/50\n",
      "1/1 - 0s - loss: 0.7977 - acc: 0.7786 - val_loss: 0.8204 - val_acc: 0.8300\n",
      "Epoch 22/50\n",
      "1/1 - 0s - loss: 0.7532 - acc: 0.8000 - val_loss: 0.7979 - val_acc: 0.8300\n",
      "Epoch 23/50\n",
      "1/1 - 0s - loss: 0.6718 - acc: 0.8286 - val_loss: 0.7771 - val_acc: 0.8320\n",
      "Epoch 24/50\n",
      "1/1 - 0s - loss: 0.6807 - acc: 0.8286 - val_loss: 0.7592 - val_acc: 0.8340\n",
      "Epoch 25/50\n",
      "1/1 - 0s - loss: 0.7164 - acc: 0.8071 - val_loss: 0.7433 - val_acc: 0.8320\n",
      "Epoch 26/50\n",
      "1/1 - 0s - loss: 0.6245 - acc: 0.8143 - val_loss: 0.7288 - val_acc: 0.8320\n",
      "Epoch 27/50\n",
      "1/1 - 0s - loss: 0.6656 - acc: 0.8214 - val_loss: 0.7148 - val_acc: 0.8300\n",
      "Epoch 28/50\n",
      "1/1 - 0s - loss: 0.7266 - acc: 0.8143 - val_loss: 0.7021 - val_acc: 0.8240\n",
      "Epoch 29/50\n",
      "1/1 - 0s - loss: 0.6705 - acc: 0.8000 - val_loss: 0.6909 - val_acc: 0.8300\n",
      "Epoch 30/50\n",
      "1/1 - 0s - loss: 0.5488 - acc: 0.8786 - val_loss: 0.6806 - val_acc: 0.8240\n",
      "Epoch 31/50\n",
      "1/1 - 0s - loss: 0.5298 - acc: 0.8857 - val_loss: 0.6710 - val_acc: 0.8240\n",
      "Epoch 32/50\n",
      "1/1 - 0s - loss: 0.5475 - acc: 0.8571 - val_loss: 0.6619 - val_acc: 0.8240\n",
      "Epoch 33/50\n",
      "1/1 - 0s - loss: 0.5767 - acc: 0.8571 - val_loss: 0.6521 - val_acc: 0.8220\n",
      "Epoch 34/50\n",
      "1/1 - 0s - loss: 0.4643 - acc: 0.8929 - val_loss: 0.6424 - val_acc: 0.8220\n",
      "Epoch 35/50\n",
      "1/1 - 0s - loss: 0.5157 - acc: 0.8929 - val_loss: 0.6344 - val_acc: 0.8260\n",
      "Epoch 36/50\n",
      "1/1 - 0s - loss: 0.4797 - acc: 0.8714 - val_loss: 0.6267 - val_acc: 0.8260\n",
      "Epoch 37/50\n",
      "1/1 - 0s - loss: 0.5801 - acc: 0.8643 - val_loss: 0.6205 - val_acc: 0.8280\n",
      "Epoch 38/50\n",
      "1/1 - 0s - loss: 0.4281 - acc: 0.9000 - val_loss: 0.6144 - val_acc: 0.8280\n",
      "Epoch 39/50\n",
      "1/1 - 0s - loss: 0.4986 - acc: 0.8571 - val_loss: 0.6089 - val_acc: 0.8340\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen,\n",
    "    epochs=50,\n",
    "    validation_data=val_gen,\n",
    "    verbose=2,\n",
    "    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph\n",
    "    callbacks=[es_callback, mc_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload the saved weights of the best model found during the training (according to validation accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"logs/best_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the best model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.8739\n",
      "\tacc: 0.8312\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_gen = generator.flow(all_nodes)\n",
    "all_predictions = model.predict_generator(all_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for full-batch methods the batch size is 1 and the predictions have shape $(1, N_{nodes}, N_{classes})$ so we we remove the batch dimension to obtain predictions of shape $(N_{nodes}, N_{classes})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions.squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few predictions after training the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31336</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061127</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106406</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37879</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126012</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107140</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102850</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31349</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106418</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123188</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128990</th>\n",
       "      <td>subject=Genetic_Algorithms</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109323</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217139</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31353</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32083</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126029</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118017</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49482</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753265</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Predicted                    True\n",
       "31336           subject=Neural_Networks         Neural_Networks\n",
       "1061127           subject=Rule_Learning           Rule_Learning\n",
       "1106406  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012   subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1107140   subject=Probabilistic_Methods                  Theory\n",
       "1102850         subject=Neural_Networks         Neural_Networks\n",
       "31349           subject=Neural_Networks         Neural_Networks\n",
       "1106418                  subject=Theory                  Theory\n",
       "1123188         subject=Neural_Networks         Neural_Networks\n",
       "1128990      subject=Genetic_Algorithms      Genetic_Algorithms\n",
       "109323    subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "217139               subject=Case_Based              Case_Based\n",
       "31353           subject=Neural_Networks         Neural_Networks\n",
       "32083           subject=Neural_Networks         Neural_Networks\n",
       "1126029  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "1118017         subject=Neural_Networks         Neural_Networks\n",
       "49482           subject=Neural_Networks         Neural_Networks\n",
       "753265          subject=Neural_Networks         Neural_Networks"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data[\"subject\"]})\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "Evaluate node embeddings as activations of the output of the 1st GraphAttention layer in GAT layer stack (the one before the top classification layer predicting paper subjects), and visualise them, coloring nodes by their true subject label. We expect to see nice clusters of papers in the node embedding space, with papers of the same subject belonging to the same cluster.\n",
    "\n",
    "Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. We find the embedding layer by taking the first graph attention layer in the stack of Keras layers. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding layer: graph_attention_sparse, output shape (1, 2708, 64)\n"
     ]
    }
   ],
   "source": [
    "emb_layer = next(l for l in model.layers if l.name.startswith(\"graph_attention\"))\n",
    "print(\n",
    "    \"Embedding layer: {}, output shape {}\".format(emb_layer.name, emb_layer.output_shape)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=emb_layer.output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The embeddings can now be calculated using the predict_generator function. Note that the embeddings returned are 64 dimensional features (8 dimensions for each of the 8 attention heads) for all nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2708, 64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict_generator(all_gen)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their true subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the embeddings from the GAT model have a batch dimension of 1 so we `squeeze` this to get a matrix of $N_{nodes} \\times N_{emb}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb.squeeze()\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=list(G.nodes()))\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GAT embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
